AI MACHINE LEARNING FOR SALES FORECASTING

Welcome back to another one of our exciting webinar blogs to recap what you may have missed! Your busy schedule may have kept you from watching our recent webinar live, “AI Machine Learning for Sales Forecasting” but that is what we have this blog for! To make sure that you can catch up on what you missed and stay up to date just like our attendees! If you would like to skip straight ahead and watch the webinar now you can click on the link below, or you can read on to get a glimpse of what it is about before watching. 

 

Link to webinar

Our Presenters 

Our two presenters for this webinar were, Sam Khan, the practice lead for our AI (Artificial Intelligence) and IoT (INTERNET OF THINGS) team with over 15 years of experience in practices such as artificial intelligence, machine learning, and more! Our second presenter was none other than Judd Halenza, our client engagement manager, with over 10 years of experience in software sales. This dynamic duo was able to present an exciting and informative webinar that you would not want to miss! Read on to learn more about what our online seminar covered. 

Webinar Outline 

Here is a brief outline of what our webinar will cover: 

  • Key Challenges when forecasting demand in retail.  
  • How would AI/ ML help in an ideal world?  
  • Microsoft Azure and Predictive Modeling  
  • Sales Forecasting Example (Food & Beverage)  
  • Demo and Q&A - On building a customized retail demand forecast with AI Consulting Services of AlphaBOLD  

Industry Challenges Our Webinar Covers 

Let us look at one of these topics that the webinar will cover in a little more detail. As you may know, AI machine learning can bring advantages to many industries and address the challenges that they face. One of these industries that we look at in our webinar is the retail industry, and we look at some of the forecasting challenges that individuals in this industry often come across. Take a look below: 

  1. The affect public holidays, and planned sales events may have on sales before the events begin.  
  2. Lowered sales resulting from the over-fulfilled demand, after a sales event.  
  3. The order size may be affected by the sales cycle, as sales representatives work to meet their quotas before the end of the cycle.  
  4. If the sales quota is met in the current cycle, then the sales may be postponed till the next cycle. 
  5. The sales events of competitors may have an influence on the sale of related products. 
  6. Promotions may impact sales of products like those on sale.  
  7. There may be similarities between products based upon historical sale patterns and inherent features. 
  8. Distinguishing whether a change in the sales volume, is a result of seasonal patterns or an irregularity, is difficult when there is no historical data.   
  9. A new product may be following growth patterns that are comparable to related products, but the number of sales may be different due to varying launch times, inherent characteristics, etc.  
  10. If sales of a specific product are increasing, it can be difficult to predict how long the trend will last.  

Conclusion 

Do these challenges sound familiar to you? Worry not, we address each one of them in our webinar and how AI machine learning can help you combat them! Learn how you can improve the quality of your sales forecasting with AI machine learning by watching our webinar today!  

 

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